phd machine learning italy

Dottorato Nazionale in Intelligenza Artificiale

The agreement for the Italian National PhD Program in Artificial Intelligence has been extended for three more cycles: 39th (2023/2024), 40th (2024/2025) and 41st (2025/2026).

Calls for admissions to the National PhD in Artificial Intelligence (PhD-AI.it) are now open!

The Italian National PhD Program in Artificial Intelligence is made of 5 federated PhD courses that bring together 61 universities and research institutions. The 5 PhD courses share a common basis in the foundations and developments of AI, and each one has an area of specialisation in a strategic sector of AI application. Each PhD course is organized by a lead university, in collaboration with the National Research Council CNR:

• Health and life sciences , Università Campus Bio-Medico di Roma
• Agrifood and environment , Università degli Studi di Napoli Federico II
• Government and Public Bodies , Sapienza Università di Roma
• Industry , Politecnico di Torino
• Society , Università di Pisa

The PhD program has a “horizontal component”, the blue area, common to all 5 PhD courses, which provides shared training opportunities on the foundational aspects of AI. Fundamental courses will be provided on consolidated topics that can be used by all PhD students, such as:

• Agent-based and Multi-agent Systems
• Computational Intelligence
• Constraints and Satisfiability
• Human Aspects in AI
• Knowledge Representation and Reasoning
• Machine Learning
• Natural Language Processing
• AI in Robotics
• Semantic Technologies

as well as courses on emerging topics related to the Trustworthy, Human-centric AI focus that characterises the EU strategy for AI, such as, for example, courses on methods for Explainable AI, Ethics and Law of AI, Safety and Robustness, Fairness, Equity, and Justice of AI systems, Distributed AI, Sustainable AI (in the double meaning of sustainable AI technologies and AI systems that help achieve the Sustainable Development Goals of the UN Agenda 2030), Social acceptability and adoption of AI.

The national PhD program in AI will train researchers, innovators and professionals, both with specialisations in the cutting-edge topics of Artificial Intelligence and in important application sectors, but ensuring an integrated and “complex” vision of the ecosystem of AI technologies and solutions, able to address problems with a systemic and multi-disciplinary approach.

PhD students in AI will participate in common training experiences and activities, both foundational and specialised, offered by the 5 PhD courses. The program covers costs for PhD student’s mobility at national and international level

The objectives of PhD-AI.it

– Build a community of young and diverse AI researchers and innovators, able to boost research as well as industrial and social innovation

– Promote the exchange of multi-disciplinary competences among the nodes of the network through the educational and research mobility of PhD students

– Integrate and strengthen the Italian network of AI research centres, also in relation to the European initiatives of the ICT-48-H2020 programme “ Towards a vibrant European network of AI excellence centres ”, in which several PhD-AI.it centres participate: Humane-AI , TAILOR , AI4MEDIA , ELISE , AI4EU .

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Ph.D. programme in Artificial Intelligence

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Programme overview

The National Ph.D. in Artificial Intelligence addresses a central theme for the digital transformation of society. Its objective is to mobilize the national community towards a comprehensive educational path centered around Artificial Intelligence (AI) and to stimulate research and industrial and social innovation in the country.

The National Ph.D. in Artificial Intelligence is part of a federation of five Ph.D. programs in AI across the country. Each program is organized by a lead university and a broad consortium of universities and research institutions. The National Ph.D. programs in AI share a common foundation focused on the fundamental aspects of AI that underlie emerging technologies transforming our society. They also offer five vertical specialization areas that aim to deepen knowledge in specific application domains, promoting multidisciplinary research:

• Health and life sciences,  Università Campus Bio-Medico di Roma
• Agricolture (agrifood) and environment,  Università degli Studi di Napoli Federico II
• Security and cybersecurity,  Sapienza Università di Roma
• Industry, Politecnico di Torino
• Society,  Università di Pisa

The general objectives of the National Ph.D. in Artificial Intelligence are:

• Build a community of young researchers specialized in the complex world of AI, promoting their distribution at both territorial and disciplinary levels.
• Foster multidisciplinary exchange of experiences among the network nodes through periods of educational and research mobility for the Ph.D. candidates.
• Integrate and strengthen the Italian network of AI research centers, aligning with the European program ICT-48-H2020 "Towards a vibrant European network of AI excellence centers" and the European strategy for AI development.

Specifically, the three-year program of the Italian National Ph.D. in Artificial Intelligence for Industry , coordinated by Politecnico di Torino, aims to provide post-graduate training for researchers, innovators, and professionals specializing in state-of-the-art AI methodologies and high-impact industrial application sectors. The Ph.D. program ensures an integrated and "complex" view of the AI technology and solution ecosystem, enabling the addressing of social challenges through a systemic and multidisciplinary approach.

The astonishing advancements in AI and robotics are profoundly and irreversibly transforming the industrial system at an unprecedented pace. The disruptive impact on all industry sectors and the economy stems from AI's ability to drive a radical transformation of digital and physical systems, making them increasingly interconnected and capable of intelligent interaction and collaboration. Italy, with its resources and potential, needs to advance both basic and applied research in AI and translate the obtained results into strategic sectors for the industry. This will optimize the growth potential that the introduction of these new technologies can bring to the national industry.

Preventive maintenance and diagnostics, next-generation automated quality control, intelligent production and adaptive management, demand-driven production, and distributed intelligence in systems based on IoT and edge/fog paradigms are key themes of Industry 4.0. Crucial aspects such as machine learning, computer vision, natural language processing, planning, and reasoning are essential to maintain the global competitiveness of the Italian industry. Simultaneously, applied research in these areas will lead to the training of AI experts ready to enter the workforce and be immediately valued—a demand that currently exceeds supply worldwide.

Key information

Type of programme:, department:, coordinator:.

DI CARLO STEFANO

Vice coordinator:

TOMMASI TATIANA

Admissions:

Titolo contacts, titolo student office.

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National PhD in Artificial Intelligence

AI for Society

Artificial Intelligence (AI) has become an integral part of our daily lives, permeating a growing number of devices and services. It is also crucial for scientific research, thanks to advanced techniques for analyzing large amounts of data. AI is driving innovation in industrial and urban policies, digitizing public administration, and revolutionizing healthcare and transportation systems. However, these opportunities also present significant challenges, both methodological and technical , as well as ethical and legal .

To address these challenges, the National PhD in Artificial Intelligence was established, with the aim of training researchers capable of developing AI systems that correspond to European ethical values, human-centric, adaptable to real environments, and able to interact in complex social contexts, promoting reliable , fair , and sustainable AI.

The National PhD Program in AI comprises 5 federated doctorates bringing together 61 organizations, including universities and other research institutions. The 5 doctorates share a common basis in the foundations and methodological aspects of AI, each developing an area of specialization in a strategic application sector of these methodologies. Each doctorate is coordinated by a lead university, in collaboration with CNR , the National Research Council. The five doctorates and the corresponding leading universities are:

• Society , University of Pisa;
• Health and Life Sciences , Campus Bio-Medico University of Rome;
• Agriculture (agrifood) and Environment , University of Naples Federico II;
• Security and Cybersecurity , Sapienza University of Rome;
• Industry 4.0 , Polytechnic University of Turin.

The existing interrelation between the “horizontal component,” common to all five doctoral programs, and the “vertical component,” different for each of them, allows the National PhD to provide an integrated and multifocal vision of Artificial Intelligence, a necessary tool to progress in research and, at the same time, to address the challenges outlined in the premise.

Following a national selection procedure in 2021, the Sant’Anna School of Advanced Studies became one of the 8 partner institutions of the PhD AI for Society (Society) coordinated by the University of Pisa . This program, whose faculty council includes numerous members of the L’EMbeDS Department of Excellence , is an integral part of a dynamic community operating in the domains of Data Science and Artificial Intelligence. In particular, the doctorate in question focuses on topics such as Human-centric AI , Explainable AI , AI for personal assistance , AI for social interaction , AI for social good , employing AI technologies and models as an innovative tool for the study of society and the complexity of social and economic phenomena.

Furthermore, the Sant’Anna School of Advanced Studies, as an associated entity, in the past cycles also funded a doctoral scholarship in the Health and Life Sciences area, whose theme concerns “Adaptive controls for hand prostheses.”

Academic Year 2024-2025 (40th Doctoral Cycle)

Sant’Anna School offers four scholarships to support research projects in the following areas:

• 1 scholarship in AI, Statistics, Computer Science and Engineering for methodological research driven by big data with applications in Economics, Management and Law , funded by the Department of Excellence L’EMbeDS (references: Francesca Chiaromonte and Andrea Vandin) - Call B ( deadline June 20, 2024 );
• 2 scholarships in Scientific Machine Learning for Urban Microclimate Surrogate Modelling , funded by the ERC StG DANTE project (reference: Giovanni Stabile) - Call B ( deadline June 20, 2024 );
• 1 scholarship in AI , Statistical Learning and Machine Learning methodologies for complex real-time data , co-funded by PNRR funds and a major industrial partner, FAMECCANICA.Data S.p.A , part of the Angelini group (references: Francesca Chiaromonte and Andrea Vandin for L’EMbeDS, Enrico Iavazzo for FAMECCANICA) - Call C ( deadline August 22, 2024 ).

Image artificially generated with Microsoft Copilot and DALL E 3 technology

• Prof.ssa Francesca Chiaromonte (Reference for Scuola Superiore Sant'Anna) [email protected]
• Prof. Andrea Vandin (Reference for Scuola Superiore Sant'Anna) [email protected]

The ADSAI PhD program

“Artificial Intelligence is the new electricity.” Andrew Ng, Co-founder and lead of Google Brain.

The Applied Data Science and Artificial Intelligence (ADSAI) PhD program at the Univeristy of Trieste started with the 2021 cohort , and since 2021 ADSAI is also part of the Italian National PhD program in Artificial Intelligence .

• Frequenty Asked Questions (FAQ) .
• Contact: [email protected]

Motivation and objectives

Why a PhD in applied Data Science and Artificial Intelligence?

The aim of the new ADSAI PhD programme is to train students to master the modern tools of Data Science and Artificial Intelligence, preparing them to create new analysis methods while giving them the expertise to apply cutting-edge tools to problems in the real world. Our research spans from theoretical to applied Machine Learning, by understanding how modern methodologies became popular we frame their current limitations and highlight their possibility of extension.

What do students achieve with the ADSAI Phd program?

ADSAI students will master modern concepts of machine learning and statistical analysis, corroborated by strong analytical problem-solving skills. For instance, graduates will be trained in deep learning and neural networks, in Bayesian methods and sampling algorithms for probabilistica graphical models, in probabilistic programming for scalable inference, high performance computing and software development.

Upon graduation, students will be proficient in delivering a complete Data Science solution to a complex real-world problem from beginning to end. Training in core disciplines will be complemented with the possibility to attend modules focusing on ethical aspects of data analysis, and the impact of technological development on regulations and society.

What is the marketplace of ADSAI students?

ADSAI graduates will be attractive for top-tier Data Science and innovation companies, and will have scientific profiles ready to enter the academic world, in Italy and abroad. Given the broad impact of Data Science and Artificial Intelligence in modern society, industry and business, graduates will have the opportunity for a fulfilling career in a variety of fields. Potential careers paths range from the financial sector to digital services, healthcare, technology companies, market research and many others.

Program structure and curricula

ADSAI is a 3-years PhD programme structured in two overlapping parts and three curricula:

[Part 1] in the first part (approx. 12 months), students spend most time training to consolidate theoretical and applied methodologies from the broad area of Data Science and Artificial Intelligence. This constitutes core knowledge-base to succesfully implement a PhD project in the second part of the programme, and serves to level differences across PhD students with different backgrounds;

[Part 2] in the second part (approx. 24 months)s, students undertake their PhD research work that culminates with a final PhD thesis at the end of the third year. Training in the last two years is intended to be more advanced and focused to the specific research area of the student.

Each student enrolls in one of three curricula, with the opportunity to pursue both pure and industry-related research questions:

• Industry and natural sciences , coordinated by Prof. Luca Bortolussi ;
• Life sciences and medicine , coordinated by Prof. Giulia Barbati ;
• Economy and society , coordinated by Prof. Domenico De Stefano .

Supervisors

The ADSAI faculty board is composed of a highly cross-disciplinary group of experts from the University of Trieste, which tightly interact with colleagues at the following institutions:

• International School for Advanced Studies (SISSA) ;
• International Centre for Theoretical Physics (ICTP) ;
• National Institute of Astrophysics (INAF) ;
• Area Science Park .

On occasions, external members include other researchers from the broader Trieste area.

What type of mentorship should a student expect?

Upon enrollment, ADSAI students identify at least one supervisor from the ADSAI faculty board . On occasion, some projects might involve the opportunity of a research placement in industry or in another research institute. In those cases, one or more external supervisors might also be available.

The supervisors will mentor the student to define the most effective training program during the whole ADSAI PhD program, taking into account the student’s background and objectives. The student will identify his/ her research question in tight collaboration with the supervisors. All students will be encouraged and supported to present their work at international conferences and meetings at the appropriate point in time of their studies.

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National PhD in Artificial Intelligence

The Italian National PhD Program in Artificial Intelligence is made of 5 federated PhD courses that bring together 61 universities and research institutions. The 5 PhD courses share a common basis in the foundations and developments of AI, and each one has an area of specialisation in a strategic sector of AI application.  The areas of specialisation are:  

• Health and Life Sciences (Lead University: Università Campus Bio-Medico di Roma)
• Industry 4.0 (Lead University: Politecnico di Torino)
• Security and Cybersecurity (Lead University: Sapienza Università di Roma
• Environment and Agricolture (Lead University: Università degli Studi di Napoli Federico II)
• Society (Lead University: Università di Pisa)

The role of the Department

The University of Trento participates in the national PhD Program in Artificial Intelligence in the AI ​​for Society area , coordinated by the University of Pisa, thanks to the scientific excellence and relevance of the Department of Information Engineering and Computer Science in the field of  Artificial Intelligence.  All details about the program are available at  https://www.phd-ai.it/en/359-2/

Area AI for Society

The study of society and the complexity of social and economic phenomena has received a strong boost in the last ten years thanks to the AI and Data Science methods, powered by the social microscope of big data analytics and social mining through inter-disciplinary hybridization with the social and economic sciences. The combination of model-driven and data-driven approaches of data mining , machine learning and network science is progressively increasing the ability to observe, measure, model and predict complex socio-economic phenomena, such as human mobility and dynamics of cities, migrations and their economic determinants, the dimensions of community well-being, the formation and dynamics of opinions and online conversations, and the social impact of AI systems. This scientific line is interlaced with the Human-centric AI one, meaning the development of advanced forms of person-machine interaction capable of improving the quality of individual and collective decisions in delicate fields, from health to justice, to economic transactions, to risk assessment in various social and economic areas. The AI ​​for Society specialization area will focus on crucial issues such as explainable AI, AI for personal assistance , AI for social interaction , AI for social good , following an approach aimed at incorporating shared ethical values ​​in AI systems ( ethics-by-design ) and to achieve common goals, with a view to sustainability, diversity, respect for human dignity and autonomy, inclusiveness and social acceptability.

Call for admissions

The call for admissions to the "AI for Society" area, for a.y. 2024-2025 is now open! Apply by June 20 th , 2024, h 1:00 pm CEST at :   https://dottorato.unipi.it/ind ex.php/it/concorsi-d-ammission e-a-a-2024-2025/item/847.html

Specific co-financed Projects - cycle 40 - a.y. 2024-2025

The University of Trento is involved in cycle 40 th of area "AI for Society" by co-financing two specific projects:

1. Latent Diffusion Models for Attribute-preserving Content Anonymization and Generation The research will address the problem of content anonymization, so as the privacy of those depicted is protected, while at the same time the dataset remains useful for downstream tasks. The approach will use a Latent Diffusion Model (LDM) and should address the limitations of the existing state-of-the-art (SOTA) approaches, namely that they (i) require either time consuming image-level latent code optimisation or costly training of additional purpose trained neural networks, and (ii) transfer background and other identifiable features from the real image. Contact: prof. Nicu Sebe ( niculae.sebe [at] unitn.it )

2. Towards Trustworthy Foundation Models The project will focus on the development of approaches to enhance the trustworthiness and reliability of Foundation Models. The directions that will be explored range from automated prompt engineering and hallucination detection strategies, to explainability-based and neuro-symbolic approaches. Contact: prof. Andrea Passerini ( andrea.passerini [at] unitn.it )

Specific co-financed Projects - cycle 39 - a.y. 2023-2024

The University of Trento is involved in cycle 39 th of area "AI for Society" by co-financing two specific projects:

1.  Operational Optimal Planning for Healthcare Coordination The goal of this PhD scholarship will be to develop novel advanced planning and scheduling algorithms leveraging neuro-symbolic hybrid approaches to orchestrate and coordinate the activities within the healthcare system, by ensuring robustness and resilience to contingencies, accounting for multi-objective cost functions, and eventually providing explanations about the suggested solutions. Contact: prof.  Giovanni Iacca ( giovanni.iacca [at] unitn.it ) and prof. Marco Roveri ( marco.roveri [at] unitn.it )

 2. Federated multi-targed domain adaptation Federated learning methods enable us to train machine learning models on distributed user data while preserving its privacy. However, it is not always feasible to obtain high-quality supervisory signals from users, especially for computer vision tasks. Unlike typical federated settings with labeled client data, this research will consider a more practical scenario where the distributed client data is unlabeled, and a centralized labeled dataset is available on the server. The research will also consider the server-client and inter-client domain shifts into account and pose a domain adaptation problem with one source (centralized server data) and multiple targets (distributed client data). Contact: prof. Nicu Sebe ( niculae.sebe [at] unitn.it )

Specific co-financed Projects - cycle 38 - a.y. 2022-2023

The University of Trento is involved in cycle XXXVIII of area "AI for Society" by co-financing two specific projects:

1. Tuning of music information retrieval models via evolutionary computing techniques. The PhD will focus on the application of evolutionary computing methods for the optimization of machine learning models in different music information retrieval tasks. The successful candidate will design, implement and evaluate advanced techniques merging the domains of music information retrieval and evolutionary computation, in areas such as classification of genres, emotions, audio effects, and type of instrument in large datasets of musical signals. Both offline and real-time scenarios will be investigated. Contact: prof. Luca Turchet ( luca.turchet [at] unitn.it )

2. Towards hybrid human-machine learning and decision making. The project will focus on the development of hybrid strategies combining human decision-makers and machine learning algorithms to improve the performance of the joint human-machine system. This challenging goal requires an interdisciplinary perspective, combining aspects of explainable AI, interactive machine learning, human-computer interaction, human decision-making and cognitive science. A relevant case study will be the development of hybrid strategies for effective public policy making. Contact: prof. Andrea Passerini ( andrea.passerini [at] unitn.it )

Specific co-financed Projects - cycle 37 - a.y. 2021-2022

The University of Trento is involved in cycle XXXVII of area "AI for Society" by co-financing three specific projects:

1. Human-cognition aware explainable AI  Existing research in explainable artificial intelligence is mostly focused on either explainable by-design methods or post-hoc reverse engineering approaches. This research aims to broaden the scope of explainable AI bringing the human in the loop of the learning process itself. This requires on the one hand to develop interactive approaches, where the machine and the user engage in a dialogue trying to improve their mutual understanding, and on the other hand to develop forms of explainability that are aware of the limitations and specificities of human cognition. The research will be conducted as a collaboration between DISI and CIMEC and the candidate will be jointly supervised by faculty members of the two institutions. Contact: prof. Andrea Passerini  ( andrea.passerini [at] unitn.it )

2. Deep learning models for multi-modal human behaviour analysis and synthesis This PhD project has the ambition to explore the fusion of multiple modalities (e.g. video, audio, inertial sensors, etc.) and the design of novel cross-modal deep neural network architectures to study social behaviours, social interactions, and human activities. In addition, the project will also address the challenge of exploiting deep generative models such as Generative Adversarial Networks (GANs) or Variational Autoencoders (VAEs) to generate video sequences depicting realistic human behaviours in a variety of social settings. Contact: prof. Niculae Sebe  ( niculae.sebe [at] unitn.it )

3. Social robotics for elderly assistance The project will aim to develop novel approaches for improving the perceptual capabilities of humanoid robots in the context of social interactions with elderly patients. Specifically, the project will aim to design and implement novel deep learning architectures which enable the robot to analyze human behaviours in multi-modal multi-party interactions, with the ultimate goal of building self-aware robots which understand the level of acceptance from the user. The PhD student will investigate algorithms for people detection and tracking, group analysis and facial expression recognition. These activities will require the design of specialized deep networks which operate in a resource-constrained setting and which permits the robot to adapt its internal knowledge to dynamic environments. Contact: prof. Elisa Ricci ( e.ricci [at] unitn.it )

For further information on the call, candidates are invited to check the webpages available in the "Useful Links" box.

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NATIONAL PHD IN ARTIFICIAL INTELLIGENCE

• ARTIFICIAL INTELLIGENCE

Presentation

© Università degli Studi di Roma "La Sapienza" - Piazzale Aldo Moro 5, 00185 Roma

Admissions to 2024-2025 PhD Programmes

Call for applications for admission to phd programmes  of national interest (din) - academic year 2024/2025 - 40th cycle .

Call for PhD programmes of National Interest (DIN) in HERITAGE SCIENCE – EARTH OBSERVATION – ARTIFICIAL INTELLIGENCE – PEACE STUDIES has been published.

Applications must be submitted by 10 July 2024 at 23:59 pm (local time) without any exception.

Call for applications DIN PhD programmes 40th cycle - 2024_2025 (pdf)  Applications Submition Tutorial - 40th cycle_2024_2025 (pdf)

CALL FOR APPLICATIONS FOR ADMISSION TO PHD PROGRAMMES - Academic Year 2024/2025 - 40th Cycle 

Sapienza University of Rome announces the following exam-based open call for admission to the 40th Cycle of PhD Programmes. The legal duration of all PhD programmes is three years.  Applications must be submitted by 20 June 2024 at 23:59 pm (local time) without any exception.

Call for applications 40th cycle - 2024_2025  (pdf) Applications Submition Tutorial - 40th cycle_2024_2025 (pdf)

CALL FOR PRE-SELECTION FOR CITIZENS OF THE PEOPLE’S REPUBLIC OF CHINA - XL CYCLE - ACADEMIC YEAR 2024/2025

Sapienza University of Rome published a call for pre-selection for postgraduate PhD positions with scholarships fund by China Scholarship Council . List of PhD Programmes for which it is possible to apply are indicated in the Appendix CSC XL cycle. The deadline for submission of the application is February 28th, 2024 at 12.00 a.m. (CET).

PLEASE NOTE THAT THIS PRE-SELECTION CALL IS RESEVE ONLY TO CITIZENS OF THE PEOPLE’S REPUBLIC OF CHINA.

Call for pre-selection CSC XL  -  Appendix CSC XL   -   Agreement Sapienza-CSC

Le attività presentate in questa pagina sono finanziate totalmente, o in parte, dai fondi del Piano nazionale di ripresa e resilienza.

L'elenco e le finalità delle attività intraprese dalla Sapienza sono descritte nella pagina dedicata Sapienza per il Pnrr .

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81610 - Machine Learning

Academic year 2023/2024.

• Docente: Andrea Asperti
• SSD: INF/01
• Language: English
• Teaching Mode: Traditional lectures
• Campus: Bologna
• Corso: Second cycle degree programme (LM) in Computer Science (cod. 5898)
• Teaching resources on Virtuale

Course Timetable

from Sep 18, 2023 to Dec 15, 2023

Learning outcomes

Machine learning deals with computer programs that extract features from data, and use them to solve predictive tasks, such as document classification, object recognition, anomaly detection, medical diagnosis, robot control, and so on. These programs, typically improve their performance through experience; they adapt to new tasks, related to previously encountered ones, solving them more efficiently. The course cover traditional topics such as decision tree learning, logistic regression, Bayesian networks and neural networks and introduces the recent field of deep learning.

Course contents

The course is divided into two main sections.

The initial part offers a comprehensive introduction to the field of machine learning, covering its typical forms: supervised, unsupervised, and reinforcement learning. It will delve into fundamental topics like decision tree learning, logistic regression, Bayesian networks, and Support Vector Machines.

The second segment of the course focuses specifically on Neural Networks and their prominent learning mechanism, the backpropagation algorithm. Students will explore various types of neural networks, including feedforward, convolutional, and recurrent networks, along with practical applications. Additionally, the course will delve into techniques for visualizing the impact of hidden units, which is closely related to concepts like deep dreams and inceptionism. Furthermore, students will be introduced to modern generative approaches, comprising Diffusion Models. The course will also briefly touch upon thematic topics such as Object Detection and Semantic Segmentation

Readings/Bibliography

Teacher's slides. During the course, additional links to relevant documents and sites will be provided.

Teaching methods

Frontal lessons integrated with practical exemplifications

We also foresee additional laboratories held by tutors.

Assessment methods

Individual project on a topic defined by the teacher, possibly integrated by a written quiz.

Teaching tools

The course will make use of several opens source libraries for Machine Learning. In particular we shall mostly use

• scikit-learn
• Tensorflow/Keras

Office hours

See the website of Andrea Asperti

• Application
• Data Science Colloquia

Machine Learning

Provided by: , from: , lecturers: , hours: , educational goals: , in evidence, academic year 2021-2022 (37th cycle).

As the Italian Ministry has decided to invest in a doctoral program on Artificial Intelligence, next year the Data Science Ph.D. will become one of the 5 nodes of a new national initiative: the National Artificial Intelligence Ph.D. All the partner institutions of the current program will join the new Ph.D.

The call for admissions to the National PhD in Artificial Intelligence is now open!

Interested in a multi-disciplinary PhD course oriented at cutting-edge research in human-centered Artificial Intelligence and its impacts on society? Apply to one of 44 fully-funded positions at the National PhD in AI – “Society” area:

https://dottorato.unipi.it/index.php/en/application-process-for-the-acad...

Deadline: July 23, 2021, h 13:00 CET

The program is launched by the University of Pisa in partnership with:

- National Research Council - CNR - Scuola Superiore Sant’Anna - Scuola Normale Superiore - Scuola IMT Lucca - Università di Firenze - Università di Modena e Reggio Emilia - Università di Siena - Università di Trento

and in collaboration with:

- Università di Bari - Università di Bologna - Università Cattolica del Sacro Cuore - Università dell’Aquila - Università degli Studi di Napoli L’Orientale - Università di Sassari - Università di Trieste - INDAM (Istituto Nazionale di Alta Matematica “Francesco Severi”) - Open Fiber SpA

This opportunity is part of the Italian National PhD Program in Artificial Intelligence. Overall, PhD-AI.it is made of 5 federated PhD courses that bring together 61 Italian universities and research institutions. The 5 PhD courses share a common basis in the foundations and developments of AI, and each one has an area of specialisation in a strategic sector of AI application. Each PhD course is organized by a lead university, in collaboration with the National Research Council CNR:

- Health and life sciences, Università Campus Bio-Medico di Roma - Agrifood and environment, Università degli Studi di Napoli Federico II - Security and cybersecurity, Sapienza Università di Roma - Industry 4.0, Politecnico di Torino - Society, Università di Pisa

Link to the calls for admissions to all the 5 PhD course are available at http://www.PhD-AI.it

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Junior University Researcher with fixed-term contract - Information processing systems (Prof. Lanz, Oswald; Montali, Marco)

CallRector's Decree n. 1100/2024 of 02.07.2024Call (pdf)Decree re-opeining n. 1480/2024 of 29.09.2024 (pdf)Online application (link)ProjectD2PAM - Data-driven Procedural Activity Mining from Multi-modal Streams (Funded by European Social Fund Plus...

Assistant Professor in Statistics - Department of Decision Sciences

Department of Decision Sciences, Bocconi University, Milan, ItalyDescriptionThe Department of Decision Sciences of Bocconi University in Milan, invites applications for aTenure-track position at the Assistant Professor level in Statistics, with a ...

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Junior university researcher with fixed-term contract - information processing systems (prof. janes, andrea).

CallRector's Decree n. 1237/2024 of 23.07.2024Call (pdf)Online application (link)ProjectDesign, Evaluation, and Deployment of Composable Service and IoT ArchitecturesDeadline for sending applications and publications08.10.2024 at 12:00 PM (noon)Nu...

Professorship in Computer Science

The Faculty of Engineering at the Free University of Bozen-Bolzano (Italy) invites applications for a faculty professorship in Computer Science (Italian scientific sectors INFO-01/A Informatics). The tenured position is offered at the level of ful...

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Up to 20 Jean Monnet Fellowships at the Robert Schuman Centre for Advanced Studies

The Robert Schuman Centre for Advanced Studies offers Jean Monnet Fellowships for the academic year 2025/26 for scholars who have obtained their doctorate more than 5 years prior to the start of the fellowship, i.e. before 1 September 2020. The Fe...

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12 PhD positions - Marie Skłodowska-Curie Doctoral Candidates within the “MetacMed - Acoustic and mechanical metamaterials for biomedical and energy harvesting applications” Doctoral Network

MetacMed is a Marie Skłodowska-Curie Action (MSCA) Doctoral Network (DN) that will provide world-class interdisciplinary training to 12 Doctoral Candidates (DCs) in the area of “Acoustic and mechanical metamaterials for biomedical and energy harve...

Discover related jobs: Information Systems (Business Informatics) jobs in Italy

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• University of Milan
• Posted on: 7 July 2023

PhD Fellowship (3 years) in Machine Learning (Milan)

Job information, offer description.

The University of Milan is seeking to appoint a PhD student for 3 years within the INTERSECTORIAL INNOVATION doctoral program. The selected candidate will work together with Prof. De Bianchi's group on the project DICOuni to develop a prototype of an AI-based search machine based on multimodal machine learning. The ideal candidate must have good knowledge of Java and/or Python; statistics; knowledge of Design Patterns, testing principles and software design; knowledge of SQL is required together with excellent command of English. The PhD student will have the opportunity to spend up to 6 months in a prestigious university in Germany and to spend 6 months in a company in Italy. Furthermore, the PhD candidate will take part in activities and courses offered by the Department of Physics, Mathematics, Philosophy and Computer Science.

Where to apply

Requirements.

The ideal candidate must have good knowledge of Java and/or Python; statistics; knowledge of Design Patterns, testing principles and software design; knowledge of SQL is required together with excellent command of English.

What is desirable:

Good knowledge of Deep Learning and model transformers (e.g. Bert, Gpt…)

Knowledge of Kubernetes and Docker

Knowledge of Bash scripting language

Knowledge of Solr, Elastic Search, and Lucene

Message queueing system (e.g. Apache kafka, rabbit Mq, ...)

Good knowledge of Spring or Python 

Good knowledge of integration techniques, such as REST API

Additional Information

Work location(s), share this page.

Computer Vision – ECCV 2024

18th European Conference, Milan, Italy, September 29–October 4, 2024, Proceedings, Part I

• Conference proceedings
• © 2025
• Aleš Leonardis   ORCID: https://orcid.org/0000-0003-0773-3277 0 ,
• Elisa Ricci   ORCID: https://orcid.org/0000-0002-0228-1147 1 ,
• Stefan Roth   ORCID: https://orcid.org/0000-0001-9002-9832 2 ,
• Olga Russakovsky   ORCID: https://orcid.org/0000-0001-5272-3241 3 ,
• Torsten Sattler   ORCID: https://orcid.org/0000-0001-9760-4553 4 ,
• Gül Varol   ORCID: https://orcid.org/0000-0002-8438-6152 5

University of Birmingham, Birmingham, UK

You can also search for this editor in PubMed   Google Scholar

University of Trento, Trento, Italy

Technical university of darmstadt, darmstadt, germany, princeton university, princeton, usa, czech technical university in prague, prague, czech republic, école des ponts paristech, marne-la-vallée, france.

Part of the book series: Lecture Notes in Computer Science (LNCS, volume 15059)

Included in the following conference series:

• ECCV: European Conference on Computer Vision

Conference proceedings info: ECCV 2024.

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About this book

The multi-volume set of LNCS books with volume numbers 15059 up to 15147 constitutes the refereed proceedings of the 18th European Conference on Computer Vision, ECCV 2024, held in Milan, Italy, during September 29–October 4, 2024.

The 2387 papers presented in these proceedings were carefully reviewed and selected from a total of 8585 submissions. They deal with topics such as computer vision; machine learning; deep neural networks; reinforcement learning; object recognition; image classification; image processing; object detection; semantic segmentation; human pose estimation; 3d reconstruction; stereo vision; computational photography; neural networks; image coding; image reconstruction; motion estimation.

• artificial intelligence
• computer networks
• computer systems
• computer vision
• Human-Computer Interaction (HCI)
• image analysis
• image coding
• image processing
• image reconstruction
• image segmentation
• machine learning
• object recognition
• pattern recognition
• reconstruction
• signal processing
• software engineering

Table of contents (27 papers)

Front matter, is retain set all you need in machine unlearning restoring performance of unlearned models with out-of-distribution images.

• Jacopo Bonato, Marco Cotogni, Luigi Sabetta

Octopus: Embodied Vision-Language Programmer from Environmental Feedback

• Jingkang Yang, Yuhao Dong, Shuai Liu, Bo Li, Ziyue Wang, Haoran Tan et al.

FunQA: Towards Surprising Video Comprehension

• Binzhu Xie, Sicheng Zhang, Zitang Zhou, Bo Li, Yuanhan Zhang, Jack Hessel et al.

4D Contrastive Superflows are Dense 3D Representation Learners

• Xiang Xu, Lingdong Kong, Hui Shuai, Wenwei Zhang, Liang Pan, Kai Chen et al.

ItTakesTwo: Leveraging Peer Representations for Semi-supervised LiDAR Semantic Segmentation

• Yuyuan Liu, Yuanhong Chen, Hu Wang, Vasileios Belagiannis, Ian Reid, Gustavo Carneiro

Ponymation: Learning Articulated 3D Animal Motions from Unlabeled Online Videos

• Keqiang Sun, Dor Litvak, Yunzhi Zhang, Hongsheng Li, Jiajun Wu, Shangzhe Wu

Robust Fitting on a Gate Quantum Computer

• Frances Fengyi Yang, Michele Sasdelli, Tat-Jun Chin

H-V2X: A Large Scale Highway Dataset for BEV Perception

• Chang Liu, Mingxu Zhu, Cong Ma

Learning Camouflaged Object Detection from Noisy Pseudo Label

• Jin Zhang, Ruiheng Zhang, Yanjiao Shi, Zhe Cao, Nian Liu, Fahad Shahbaz Khan

Weakly Supervised 3D Object Detection via Multi-level Visual Guidance

• Kuan-Chih Huang, Yi-Hsuan Tsai, Ming-Hsuan Yang

Deblur e -NeRF: NeRF from Motion-Blurred Events under High-speed or Low-light Conditions

• Weng Fei Low, Gim Hee Lee

CLR-GAN: Improving GANs Stability and Quality via Consistent Latent Representation and Reconstruction

• Shengke Sun, Ziqian Luan, Zhanshan Zhao, Shijie Luo, Shuzhen Han

Learn from the Learnt: Source-Free Active Domain Adaptation via Contrastive Sampling and Visual Persistence

• Mengyao Lyu, Tianxiang Hao, Xinhao Xu, Hui Chen, Zijia Lin, Jungong Han et al.

PromptIQA: Boosting the Performance and Generalization for No-Reference Image Quality Assessment via Prompts

• Zewen Chen, Haina Qin, Juan Wang, Chunfeng Yuan, Bing Li, Weiming Hu et al.

Motion Mamba: Efficient and Long Sequence Motion Generation

• Zeyu Zhang, Akide Liu, Ian Reid, Richard Hartley, Bohan Zhuang, Hao Tang

Radiative Gaussian Splatting for Efficient X-Ray Novel View Synthesis

• Yuanhao Cai, Yixun Liang, Jiahao Wang, Angtian Wang, Yulun Zhang, Xiaokang Yang et al.

Tracking Meets LoRA: Faster Training, Larger Model, Stronger Performance

• Liting Lin, Heng Fan, Zhipeng Zhang, Yaowei Wang, Yong Xu, Haibin Ling

A Direct Approach to Viewing Graph Solvability

• Federica Arrigoni, Andrea Fusiello, Tomas Pajdla

CoR-GS: Sparse-View 3D Gaussian Splatting via Co-regularization

• Jiawei Zhang, Jiahe Li, Xiaohan Yu, Lei Huang, Lin Gu, Jin Zheng et al.

Other volumes

Editors and affiliations.

Aleš Leonardis

Elisa Ricci

Stefan Roth

Olga Russakovsky

Torsten Sattler

Bibliographic Information

Book Title : Computer Vision – ECCV 2024

Book Subtitle : 18th European Conference, Milan, Italy, September 29–October 4, 2024, Proceedings, Part I

Editors : Aleš Leonardis, Elisa Ricci, Stefan Roth, Olga Russakovsky, Torsten Sattler, Gül Varol

Series Title : Lecture Notes in Computer Science

DOI : https://doi.org/10.1007/978-3-031-73232-4

Publisher : Springer Cham

eBook Packages : Computer Science , Computer Science (R0)

Copyright Information : The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2025

Softcover ISBN : 978-3-031-73231-7 Due: 31 October 2024

eBook ISBN : 978-3-031-73232-4 Published: 29 September 2024

Series ISSN : 0302-9743

Series E-ISSN : 1611-3349

Edition Number : 1

Number of Pages : LXXXV, 497

Number of Illustrations : 6 b/w illustrations, 188 illustrations in colour

Topics : Computer Imaging, Vision, Pattern Recognition and Graphics , Signal, Image and Speech Processing , Computer Communication Networks , User Interfaces and Human Computer Interaction , Machine Learning , Special Purpose and Application-Based Systems

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